DIFFERENT BETWENN IN BUILD PLOT FUNCTION AND GGPLOT

# LOADING THE ESSENTIAL LIBRARIES
library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# THE NORMAL PLOT USING PLOT FUNCTION

## HERE IS THE CSV ON CHINA AND INDIA GDP GROWTH RATE FROM 1961 TO 2022
df<-read.csv("D:/Acer/ww.csv")

# looking for the structure of the dataframe
str(df)

## 'data.frame':    63 obs. of  3 variables:
##  $ Year : int  1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
##  $ China: num  -27.27 -5.58 10.3 18.18 16.95 ...
##  $ India: num  3.72 2.93 5.99 7.45 -2.64 ...

head(df,5)

##   Year  China     India
## 1 1960 -27.27  3.722743
## 2 1961  -5.58  2.931128
## 3 1962  10.30  5.994353
## 4 1963  18.18  7.452950
## 5 1964  16.95 -2.635770

# The plot 
plot(df$Year, df$China, type = "l", col = "red", 
    main = "China vs India", xlab = "Year", ylab = "gdp growth rate")
lines(df$Year, df$India, type = "l", col = "blue")
legend("bottomright", legend = c("China", "India"), col = c("red", "blue"), lty = 1)

# the graph is simple and clean. The inbuild function in R is very simple plotting of the data



# GGPlOT

#loading the ggplot library
library(ggplot2)

# before plotting the graph , I will make it in bit more presesntable manner

new_df <- pivot_longer(df,cols = c("China","India"),
                    names_to = "country",values_to = "growth_rate")

head(new_df,5)

## # A tibble: 5 × 3
##    Year country growth_rate
##   <int> <chr>         <dbl>
## 1  1960 China        -27.3 
## 2  1960 India          3.72
## 3  1961 China         -5.58
## 4  1961 India          2.93
## 5  1962 China         10.3

# by using the pivot_longer function  i have merged the two colums together a single colum with countrries 

# the plot
plot <- ggplot(data = new_df, aes(x = Year, y = growth_rate, colour = country))+
geom_line() +
labs(title = "CHINA VS INDIA 1961", x = "Years", y = "GDP Growth Rate")

plot

# library ggplot make the plot more stand out and also add more functionality the graph


# for instance we can point in out graph

plot_point <-  ggplot(data = new_df, aes(x = Year, y = growth_rate, colour = country))+
geom_line() +
geom_point()+  
labs(title = "CHINA VS INDIA 1961", x = "Years", y = "GDP Growth Rate")

plot_point

# we can make this more plot more advanced and interactive using plotly library
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

plot_interactive <-  ggplot(data = new_df, aes(x = Year, y = growth_rate, colour = country,group=country,text=paste("Year:",Year,"<br>Growth_rate",round(growth_rate,2))))+
geom_line() +
geom_point()+  
labs(title = "CHINA VS INDIA 1961", x = "Years", y = "GDP Growth Rate")


plot_interactive

ggplotly(plot_interactive,tooltip = "text")